Efficient modeling of liquid splashing via graph neural networks with adaptive filter and aggregator fusion
Jinyao Nan,
Pingfa Feng,
Jie Xu
et al.
Abstract:Purpose
The purpose of this study is to advance the computational modeling of liquid splashing dynamics, while balancing simulation accuracy and computational efficiency, a duality often compromised in high-fidelity fluid dynamics simulations.
Design/methodology/approach
This study introduces the fluid efficient graph neural network simulator (FEGNS), an innovative framework that integrates an adaptive filtering layer and aggregator fusion strategy within a graph neural network architecture. FEGNS is designed… Show more
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